Pogled na podatke nakon čišćenja i transformacija¶

In [ ]:
import numpy as np
import pandas as pd
from geopy.distance import geodesic
import numpy as np
import matplotlib.pyplot as plt
from shapely import Point
import geopandas as gpd
import warnings

warnings.filterwarnings('ignore')
In [ ]:
df = pd.read_csv('dataset/transformed_deliverytime.csv')
df.head()
Out[ ]:
Unnamed: 0 ID Delivery_person_ID Delivery_person_Age Delivery_person_Ratings Restaurant_latitude Restaurant_longitude Delivery_location_latitude Delivery_location_longitude Weatherconditions ... Vehicle_condition Type_of_order Type_of_vehicle multiple_deliveries Festival City Time_taken(min) Datetime_Ordered Datetime_Picked distance(km)
0 0 0x4607 INDORES13DEL02 37 4.9 22.745049 75.892471 22.765049 75.912471 Sunny ... 2 snack motorcycle 0 0 Urban 24 2022-03-19 11:30:00 2022-03-19 11:45:00 3.020737
1 1 0xb379 BANGRES18DEL02 34 4.5 12.913041 77.683237 13.043041 77.813237 Stormy ... 2 snack scooter 1 0 Metropolitian 33 2022-03-25 19:45:00 2022-03-25 19:50:00 20.143737
2 2 0x5d6d BANGRES19DEL01 23 4.4 12.914264 77.678400 12.924264 77.688400 Sandstorms ... 0 drinks motorcycle 1 0 Urban 26 2022-03-19 08:30:00 2022-03-19 08:45:00 1.549693
3 3 0x7a6a COIMBRES13DEL02 38 4.7 11.003669 76.976494 11.053669 77.026494 Sunny ... 0 buffet motorcycle 1 0 Metropolitian 21 2022-04-05 18:00:00 2022-04-05 18:10:00 7.774497
4 4 0x70a2 CHENRES12DEL01 32 4.6 12.972793 80.249982 13.012793 80.289982 Cloudy ... 1 snack scooter 1 0 Metropolitian 30 2022-03-26 13:30:00 2022-03-26 13:45:00 6.197898

5 rows × 21 columns

In [ ]:
def df_to_gdf(df, lat_col_name, long_col_name):
    gdf = gpd.GeoDataFrame( 
        geometry=gpd.points_from_xy(df[long_col_name], df[lat_col_name]), 
        crs="EPSG:4326"
    )

    return gdf

Pregled lokacija restorana¶

In [ ]:
restaurant_gdf = df_to_gdf(df, 'Restaurant_latitude', 'Restaurant_longitude')
restaurant_gdf.explore()
Out[ ]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Pregled lokacija dostave¶

In [ ]:
delivery_gdf = df_to_gdf(df, 'Delivery_location_latitude', 'Delivery_location_longitude')
delivery_gdf.explore()
Out[ ]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Pregled udaljenosti i vremena dostave¶

In [ ]:
distance_data = df['distance(km)']

plt.hist(distance_data, bins=20, color='skyblue', edgecolor='black')

plt.xlabel('Distance')
plt.ylabel('Frequency')
plt.title('Distance distribution')

plt.show()
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In [ ]:
time_taken_data = df['Time_taken(min)']

plt.scatter(distance_data, time_taken_data, color='skyblue', marker='o')

plt.xlabel('Distance')
plt.ylabel('Time Taken')
plt.title('Distance vs. Time Taken')

plt.show()
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In [ ]:
from scipy.stats import pearsonr

correlation = pearsonr(distance_data, time_taken_data)[0]
print(f"Pearson Correlation: {correlation:.2f} ")

Pearson Correlation: 0.32